Gastos Casa
11 August, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 29/7/2025 | Comida | 10000 | Andrés | complemento comida |
| 29/7/2025 | Electrodomésticos/mantención casa | 68000 | Andrés | NA |
| 30/7/2025 | Comida | 24140 | Tami | Supermercado |
| 31/7/2025 | Gas | 19100 | Andrés | NA |
| 3/8/2025 | Comida | 86089 | Tami | Supermercado |
| 10/8/2025 | Comida | 108649 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0208e+09 2 5.262 0.0054 **
## lag_depvar 2.6341e+11 1 2715.568 <2e-16 ***
## Residuals 8.1964e+10 845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1785.785 16243.46 0.1442934
## 2-0 31323.581 23214.473 39432.69 0.0000000
## 2-1 24094.743 19404.285 28785.20 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 693 53557.84 21967.297
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2017.44912 4039.75617 -537.42567 2438.63177 -2968.35392 519.21050
## 8 9 10 11 12 13
## -5655.16546 -1188.46860 -3966.86414 -419.40885 -4940.93886 -1611.49963
## 14 15 16 17 18 19
## -901.81220 375.62363 -3244.23198 -379.68961 -2131.58388 6602.50451
## 20 21 22 23 24 25
## -1529.07823 -1208.44416 1475.31107 -1186.41958 234.64776 1695.18959
## 26 27 28 29 30 31
## -7101.34233 946.75517 8192.17811 420.22636 -11.86174 -2398.46429
## 32 33 34 35 36 37
## 1577.74438 4574.48078 1129.99337 2394.68298 -1864.32005 4610.98791
## 38 39 40 41 42 43
## 4305.56246 -2272.77845 -2979.36227 -1108.99159 -10740.67465 7287.54286
## 44 45 46 47 48 49
## 2557.35902 1367.53946 8106.10577 688.88961 6531.34652 6718.21594
## 50 51 52 53 54 55
## -5877.38656 -4792.43420 -5058.17160 -7928.33905 6128.20684 -4076.58367
## 56 57 58 59 60 61
## -4895.23501 3853.88380 887.03349 -32.61504 141.77959 -4996.78847
## 62 63 64 65 66 67
## 18125.32153 3643.58850 -3642.66084 5927.61133 7347.48066 14643.72995
## 68 69 70 71 72 73
## 1701.00602 -13205.32503 -1302.49907 4646.55144 -4896.44505 -4402.13919
## 74 75 76 77 78 79
## -10496.16833 2465.97578 -5399.75050 1062.85618 -6866.65611 545.35189
## 80 81 82 83 84 85
## -2355.66410 -2695.12783 -3933.34993 -539.41585 2312.99992 3761.80449
## 86 87 88 89 90 91
## 476.87957 -484.49571 196.54346 4301.90408 -1162.21219 1151.32773
## 92 93 94 95 96 97
## -2063.81838 -1044.10564 177.56730 274.82356 -7483.93130 2390.54410
## 98 99 100 101 102 103
## -8603.01046 -2942.37550 -4041.27759 -1738.70919 -1263.06025 3179.50270
## 104 105 106 107 108 109
## -2342.62177 2593.22580 -1158.56822 970.19149 2587.08949 -3153.89634
## 110 111 112 113 114 115
## -4723.10797 -851.02809 1902.81481 11693.34230 -1240.69861 2670.01921
## 116 117 118 119 120 121
## 4264.68827 3505.14317 -1097.05551 -4713.87922 -3722.64884 2320.52086
## 122 123 124 125 126 127
## -1731.46081 1341.28655 8859.35491 849.74459 133.02283 -2518.87835
## 128 129 130 131 132 133
## 2656.82497 7054.63784 1015.64380 -8496.32376 1750.47728 4137.01554
## 134 135 136 137 138 139
## -3161.83629 -1418.36149 -852.94572 -3879.16722 1183.19800 -495.04680
## 140 141 142 143 144 145
## -2913.22972 1718.06824 -1880.79079 -7829.30593 2038.17254 -3480.42918
## 146 147 148 149 150 151
## 2101.03552 -258.15001 1022.39376 -359.68207 1351.80007 1186.52074
## 152 153 154 155 156 157
## 3356.68683 -4861.10877 -1174.68113 -3236.15911 5955.89753 9746.96780
## 158 159 160 161 162 163
## -3669.37405 -5015.92277 3366.17452 -42.01924 2459.60176 -6146.82382
## 164 165 166 167 168 169
## -6984.43855 3919.12619 17155.08575 3384.07736 -643.63000 -2691.33284
## 170 171 172 173 174 175
## -1349.24243 3345.33500 -474.56706 -8322.02699 2618.52089 4079.11216
## 176 177 178 179 180 181
## 377.66354 8502.04176 -9499.55761 -3720.49683 -10992.79158 -11485.98219
## 182 183 184 185 186 187
## 991.24162 9048.46095 -1678.39929 5679.90782 6303.17727 12901.38854
## 188 189 190 191 192 193
## 8165.11242 -4335.95503 2191.20445 10091.32941 -1928.73680 -2730.03904
## 194 195 196 197 198 199
## -10565.42162 -6642.40698 958.27888 -5508.96830 -10067.95238 5118.33060
## 200 201 202 203 204 205
## -3335.98076 -1977.69084 -1068.06250 6230.84881 9612.09904 298.29770
## 206 207 208 209 210 211
## 2643.14507 2813.65514 5497.61050 12542.75592 -5987.38953 -11589.97362
## 212 213 214 215 216 217
## -5949.68552 -10864.85948 -5343.18414 1263.57774 -13274.42927 16136.03575
## 218 219 220 221 222 223
## 7541.70571 1252.87180 26411.05489 12223.86689 7021.52064 13708.16404
## 224 225 226 227 228 229
## -4242.38818 -2062.85555 3460.53057 43.76741 2434.15353 8695.28420
## 230 231 232 233 234 235
## 5519.68164 -2214.53474 -2129.49855 9129.72192 -11808.85302 -7571.80480
## 236 237 238 239 240 241
## -8822.08654 -10374.64586 2812.88775 1085.00125 -8565.01362 -9250.43340
## 242 243 244 245 246 247
## 8840.97024 -8027.85919 2230.89897 -10560.76457 -4305.58237 1172.92263
## 248 249 250 251 252 253
## 749.97979 -12571.80335 3396.73517 1809.87438 3954.78585 1871.40157
## 254 255 256 257 258 259
## -1427.94010 10871.22796 20599.07037 2889.06202 -4583.40677 3802.63230
## 260 261 262 263 264 265
## -2005.43532 3429.43806 -5162.50700 -11197.37362 -5018.89990 -806.29433
## 266 267 268 269 270 271
## -5472.43002 8499.37152 -4571.59908 3902.40992 -2400.58318 4138.96142
## 272 273 274 275 276 277
## 410.20091 7002.45112 -1723.49941 11715.27046 -4912.72966 1403.29092
## 278 279 280 281 282 283
## -696.61165 7528.42277 -5391.67287 -3055.18933 -11578.54547 -2965.47532
## 284 285 286 287 288 289
## 18364.02677 7461.12053 2399.38744 -966.23792 571.62058 6064.31350
## 290 291 292 293 294 295
## 6539.23453 -19124.96895 -11450.71707 -8407.83787 9396.65731 2785.60327
## 296 297 298 299 300 301
## -1470.72048 27113.53201 9721.48220 4539.97225 9152.26991 2477.36477
## 302 303 304 305 306 307
## -1410.08697 7528.56109 -24672.49887 -3849.63685 -477.07521 -7265.39913
## 308 309 310 311 312 313
## -4249.57199 2666.03130 -9463.27872 -3477.82273 -8425.39942 1345.72741
## 314 315 316 317 318 319
## -3376.22684 1828.48855 -4310.53493 27223.60586 -1035.84160 2981.71725
## 320 321 322 323 324 325
## 10513.72922 5247.88683 32030.09726 4689.43965 -21356.10608 1439.32433
## 326 327 328 329 330 331
## 761.13437 -6809.21114 -2054.62604 -33577.78464 697.34470 -2487.35099
## 332 333 334 335 336 337
## -271.19701 -3345.87804 3915.05190 -621.11290 -7137.08579 -3283.57894
## 338 339 340 341 342 343
## -2352.93666 -7838.18432 3710.50597 -1529.52692 -1897.12877 -1153.02679
## 344 345 346 347 348 349
## 15.52609 315.16783 -1791.25883 -9619.46995 -13360.96972 2190.26510
## 350 351 352 353 354 355
## -4458.70522 -3788.90323 -6107.49295 1632.40964 1250.84102 2605.94652
## 356 357 358 359 360 361
## -3931.19205 -677.31958 510.28306 6837.35301 71.74336 -248.03552
## 362 363 364 365 366 367
## 2369.99522 -2974.24671 -1093.30288 -8957.22807 -4810.76925 -6383.72165
## 368 369 370 371 372 373
## -5103.20291 -7394.54575 4890.87526 220.31148 6960.15018 -7827.10080
## 374 375 376 377 378 379
## -2431.04269 -3552.58877 -2624.61618 -12611.63507 1785.24870 -10767.42877
## 380 381 382 383 384 385
## 5590.68571 9199.44072 2946.97690 -2595.37460 1411.41596 6540.11277
## 386 387 388 389 390 391
## 11177.94493 -6079.84894 -5623.07328 -401.32527 8317.85615 1536.70416
## 392 393 394 395 396 397
## 10936.84688 -10204.10366 2485.01504 414.40240 263.47997 -952.47282
## 398 399 400 401 402 403
## -856.93649 -14776.66558 8294.62718 -1436.18573 -1619.93286 6741.94492
## 404 405 406 407 408 409
## -8197.15287 -1527.42684 -2754.07922 -6029.15148 -3044.50631 -4091.51976
## 410 411 412 413 414 415
## -8916.04014 6003.01364 1479.36789 -7546.29000 -7840.88252 14096.12406
## 416 417 418 419 420 421
## 3624.26477 4277.19101 -8273.66088 -4950.97857 -2789.58747 2640.29911
## 422 423 424 425 426 427
## -14204.14429 -2931.94250 -9233.54601 2907.46250 6850.27002 6411.71306
## 428 429 430 431 432 433
## -4184.07864 -4304.86872 -4893.21867 -1946.04381 -5866.36213 -6764.47859
## 434 435 436 437 438 439
## -6068.84199 -1498.27175 -957.40561 -5091.28919 2475.04384 4712.30506
## 440 441 442 443 444 445
## -5211.81406 -2300.35685 1435.39607 -3991.38610 2689.93947 -6741.33570
## 446 447 448 449 450 451
## -12253.81236 -4615.86712 9548.09428 -2174.78025 4613.14857 -6034.44755
## 452 453 454 455 456 457
## -1269.75986 235.68170 2871.54612 -12438.97620 3239.43749 -6849.88130
## 458 459 460 461 462 463
## 6392.43660 2852.15369 2331.67754 -4033.21012 1917.37197 -192.99045
## 464 465 466 467 468 469
## 1605.45081 -716.95049 3155.51112 -2847.19079 5607.43802 -7160.38535
## 470 471 472 473 474 475
## -3154.46229 -2383.65780 -4834.05507 2842.02265 7629.41700 -6214.87106
## 476 477 478 479 480 481
## 1308.74051 -6360.42595 -3004.31792 1860.03116 -13091.84463 -9875.42081
## 482 483 484 485 486 487
## -1296.14943 -78.57311 -1069.87703 -1454.32458 -9700.30889 11004.28326
## 488 489 490 491 492 493
## 6097.01793 7255.25525 -5630.18694 5191.87111 9097.20544 5825.84539
## 494 495 496 497 498 499
## -13719.76279 -10760.75882 -3598.41963 -1253.14396 -670.67249 -7773.50238
## 500 501 502 503 504 505
## 486.75656 4156.66397 5358.76914 488.86365 -94.32626 -7414.90276
## 506 507 508 509 510 511
## 417.96392 -5204.61187 1691.07143 -1447.44383 -8307.23630 -727.36574
## 512 513 514 515 516 517
## -2802.87649 -712.20661 1204.31412 -9632.61443 -7875.99298 24193.93842
## 518 519 520 521 522 523
## 9643.58819 5663.75155 -5569.21247 2585.95118 16799.01745 11198.56894
## 524 525 526 527 528 529
## -24455.69713 -5275.59586 -3929.30678 4389.75580 -554.89034 -11298.87931
## 530 531 532 533 534 535
## 4233.28769 13734.44254 -5199.99354 4169.35477 5336.45437 -2025.93068
## 536 537 538 539 540 541
## -4766.89517 -7281.35386 -2281.57745 8148.97402 -74.78497 -8342.31948
## 542 543 544 545 546 547
## 1644.07683 -778.23733 189.40139 -11209.36697 -11203.57668 1932.04020
## 548 549 550 551 552 553
## 6881.44284 -1468.49884 687.41470 -7876.08694 8432.62018 741.21012
## 554 555 556 557 558 559
## -12114.79836 9029.44845 8488.56336 -102.09806 4651.60181 -3792.07396
## 560 561 562 563 564 565
## 13904.30168 21247.22097 -6786.78711 -9968.85450 6527.82406 -39.00903
## 566 567 568 569 570 571
## 3195.22629 -7641.96518 -17539.92593 6495.98496 6246.60812 1694.87459
## 572 573 574 575 576 577
## 2887.98062 1552.95188 -2383.72919 14509.70796 -9903.19930 -6461.21412
## 578 579 580 581 582 583
## 8518.43585 2637.82866 -6772.02554 7308.35176 -4023.10037 -2982.07811
## 584 585 586 587 588 589
## 15508.23058 -14744.08209 8236.23071 -147.74456 -6426.61523 -943.64501
## 590 591 592 593 594 595
## 67.26463 -10836.94506 1649.82539 -7298.71434 2935.78198 8721.42670
## 596 597 598 599 600 601
## -7676.83969 5699.71344 2562.15745 6673.85300 -3393.75103 5959.04171
## 602 603 604 605 606 607
## -8507.01166 2077.27563 1085.81503 2951.69398 1300.45882 200.64534
## 608 609 610 611 612 613
## -6005.50387 7901.46644 -1381.54166 -2764.46077 -3632.10516 -8393.61326
## 614 615 616 617 618 619
## 11820.52270 4759.35254 -9505.13658 11467.97851 5855.76565 -5782.11377
## 620 621 622 623 624 625
## 26173.67040 -13086.53940 -6989.00975 2975.18557 -4347.20714 -10763.69513
## 626 627 628 629 630 631
## 11159.04986 -21807.41596 -2522.05196 8571.01443 11001.90397 -1720.07525
## 632 633 634 635 636 637
## 33122.90993 -6842.79437 5491.09161 5164.45571 -2509.97506 -5570.44688
## 638 639 640 641 642 643
## -2141.97888 -12621.72459 -2393.56115 -2030.60371 -2659.52161 -2990.79610
## 644 645 646 647 648 649
## 1688.95485 4297.61960 16817.57943 18357.57146 639.20577 4550.63822
## 650 651 652 653 654 655
## 10368.09210 19886.75193 441.09901 -28349.98281 -1534.88435 -2472.93649
## 656 657 658 659 660 661
## 1700.05225 -3358.79555 -10774.36244 1543.02202 4101.22775 -1141.58616
## 662 663 664 665 666 667
## 12901.41412 1198.80658 1652.54324 -11852.43102 1249.23926 1055.47973
## 668 669 670 671 672 673
## -5298.90428 -7528.91499 1965.90604 -3817.92096 2574.86473 -3485.02105
## 674 675 676 677 678 679
## -9436.29787 -8388.55475 -3049.04440 100.30883 2767.97962 623.22636
## 680 681 682 683 684 685
## -3921.63736 -1899.01862 -1408.89232 -8334.11670 4569.30911 -2335.79471
## 686 687 688 689 690 691
## -1490.63688 494.36843 10756.17825 9729.01620 10485.30496 -9816.58234
## 692 693 694 695 696 697
## -3682.75232 -3259.12393 5758.73396 -10507.67844 -8012.76769 -8699.11921
## 698 699 700 701 702 703
## -6350.16211 -4809.06037 3014.62590 -4480.07258 -1973.54729 4145.32963
## 704 705 706 707 708 709
## 31019.15724 9407.73574 23335.34597 1573.70166 8225.53312 22829.67694
## 710 711 712 713 714 715
## 6476.48590 -18278.36903 4758.67962 -5503.79325 -158.20616 422.96184
## 716 717 718 719 720 721
## -17323.03470 -5319.33085 3280.00204 -3068.55408 -13033.21942 4225.91495
## 722 723 724 725 726 727
## -5610.63381 688.26532 -3989.24749 -12501.76019 1313.83681 -1921.37465
## 728 729 730 731 732 733
## -9832.07214 17214.83400 1719.16680 -2779.12781 5659.03716 -8687.23402
## 734 735 736 737 738 739
## -779.23831 8082.47822 -15408.10509 -5966.25717 7353.14856 -4840.52985
## 740 741 742 743 744 745
## 105.03403 1771.67749 -2012.24598 -5224.38752 6356.61866 -6331.70082
## 746 747 748 749 750 751
## 22642.50457 7769.64662 -2004.32676 -7344.98519 23360.43198 -4346.74707
## 752 753 754 755 756 757
## 1341.99292 -14475.77436 56055.88286 26874.97213 15056.98950 -10678.77428
## 758 759 760 761 762 763
## 10575.18467 7285.44128 5782.15330 -46415.20684 -16203.90584 930.69154
## 764 765 766 767 768 769
## -2552.99129 -3493.66735 122807.63988 19313.58670 43727.10207 22604.95322
## 770 771 772 773 774 775
## 12099.13861 15869.21567 25749.53773 -98783.23509 -6772.40093 -35850.97074
## 776 777 778 779 780 781
## 1704.09017 -1260.75540 3364.11847 -7445.03480 -1477.53774 -1977.67850
## 782 783 784 785 786 787
## 3391.98528 -7185.61982 -2269.16311 3887.07649 2235.38636 -2787.36761
## 788 789 790 791 792 793
## -4075.99929 1709.57371 2825.58520 -77.43441 -6728.86901 -5809.98389
## 794 795 796 797 798 799
## -1176.38227 -1291.91087 -7845.87453 -2384.84997 -3299.16813 -2696.95226
## 800 801 802 803 804 805
## 10692.80477 2214.63431 7054.41662 2901.20276 -5469.30110 8164.56277
## 806 807 808 809 810 811
## 9854.99705 -10618.29660 -7416.47384 -7527.59098 2981.28070 4171.25886
## 812 813 814 815 816 817
## -2290.74438 -14186.63246 -4136.66089 6232.80382 8213.22968 -9693.73945
## 818 819 820 821 822 823
## -7773.41083 -9361.50870 9726.36582 -1246.56072 -4395.31640 -8471.38801
## 824 825 826 827 828 829
## 7848.16395 7890.70630 4954.93597 -3121.45377 -732.04945 2871.57754
## 830 831 832 833 834 835
## 4648.84862 1605.72637 -6712.55127 2068.03178 -418.66411 2732.67373
## 836 837 838 839 840 841
## 4120.44664 -1155.87689 -9354.84814 5654.07368 -4881.90642 -7599.28519
## 842 843 844 845 846 847
## 2999.25952 -13065.24893 -2843.08449 11604.21755 1290.30246 614.78598
## 848 849 850
## -681.61564 4492.45933 12666.85928
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17251.84 20099.24 24353.57 24071.51 26425.07 23757.50 24473.88 19705.61
## 10 11 12 13 14 15 16 17
## 19442.15 16784.69 17562.22 14291.36 14342.53 15007.23 16703.95 15023.83
## 18 19 20 21 22 23 24 25
## 16058.58 15432.07 22515.08 21599.02 21078.83 22968.99 22294.92 22947.52
## 26 27 28 29 30 31 32 33
## 24793.63 18721.53 20447.82 28285.77 28343.43 28016.32 25645.54 27048.09
## 34 35 36 37 38 39 40 41
## 30891.44 31239.89 32649.18 30159.58 34137.44 37345.78 34401.65 31212.28
## 42 43 44 45 46 47 48 49
## 30059.96 20638.74 28158.07 30594.75 31684.04 38522.68 38017.22 42679.78
## 50 51 52 53 54 55 56 57
## 46916.39 39613.72 34181.74 29204.05 22347.94 28638.44 25218.81 21516.12
## 58 59 60 61 62 63 64 65
## 25924.82 27184.47 27481.51 27893.36 23763.96 40356.55 42200.66 37446.25
## 66 67 68 69 70 71 72 73
## 41653.52 46569.56 57238.57 55252.18 40494.21 37999.88 41018.02 35317.71
## 74 75 76 77 78 79 80 81
## 30769.60 21472.31 24674.04 20599.43 22685.66 17580.79 19596.38 18822.84
## 82 83 84 85 86 87 88 89
## 17850.49 15919.27 17197.14 20805.48 25223.55 26213.50 26238.46 26855.24
## 90 91 92 93 94 95 96 97
## 30980.64 29811.10 30810.53 28874.82 28074.58 28442.75 28849.36 22426.31
## 98 99 100 101 102 103 104 105
## 25441.58 18471.52 17327.56 15368.14 15667.92 16345.35 20818.34 19901.77
## 106 107 108 109 110 111 112 113
## 23413.14 23203.09 24879.34 27756.32 25254.25 21697.46 21972.90 24619.37
## 114 115 116 117 118 119 120 121
## 35484.70 33677.41 35515.03 38513.57 40469.63 38157.88 32978.51 29319.62
## 122 123 124 125 126 127 128 129
## 31402.60 29682.43 30864.07 38464.40 38106.83 37168.31 34031.60 35812.93
## 130 131 132 133 134 135 136 137
## 41211.21 40651.47 31852.52 33117.41 36307.41 32717.79 31104.95 30189.88
## 138 139 140 141 142 143 144 145
## 26746.66 28161.19 27930.80 25616.93 27641.51 26266.16 19867.83 22898.57
## 146 147 148 149 150 151 152 153
## 20725.11 23702.44 24242.46 25832.97 26015.06 27669.34 28970.17 32002.54
## 154 155 156 157 158 159 160 161
## 27472.40 26735.30 24290.39 30184.89 41689.80 40019.92 37384.68 42405.30
## 162 163 164 165 166 167 168 169
## 43813.97 47230.11 42695.72 38002.59 43428.20 59731.49 61943.77 60357.76
## 170 171 172 173 174 175 176 177
## 57183.24 55582.38 58285.14 57309.17 49600.76 52424.46 56167.34 56203.53
## 178 179 180 181 182 183 184 185
## 63332.84 53834.50 50585.22 41393.27 32932.04 36440.54 46544.69 46000.66
## 186 187 188 189 190 191 192 193
## 51953.82 57699.18 68482.89 73766.10 67460.37 67653.81 74724.59 70400.75
## 194 195 196 197 198 199 200 201
## 65923.28 55166.41 49196.15 50620.54 46214.95 38383.24 44808.41 43035.69
## 202 203 204 205 206 207 208 209
## 42673.63 43152.01 49946.47 58836.27 58465.85 60190.77 61846.68 65638.10
## 210 211 212 213 214 215 216 217
## 75105.25 67187.55 55375.83 49984.29 40980.04 37937.57 41051.43 31070.96
## 218 219 220 221 222 223 224 225
## 48045.58 55366.84 56268.80 79035.70 86531.19 88534.55 96126.39 87076.71
## 226 227 228 229 230 231 232 233
## 81074.76 80656.66 77306.42 76467.86 81205.18 82569.53 77004.64 72217.28
## 234 235 236 237 238 239 240 241
## 77871.28 64518.23 56554.23 48504.36 40115.40 44307.57 46460.44 39910.72
## 242 243 244 245 246 247 248 249
## 33589.89 43873.00 38119.53 42055.48 34318.87 33024.65 36680.16 39504.23
## 250 251 252 253 254 255 256 257
## 30333.12 36271.55 40073.21 45268.31 47986.80 47479.34 57780.93 75279.22
## 258 259 260 261 262 263 264 265
## 75094.26 68404.51 69886.44 66106.99 67553.22 61310.52 50584.47 46611.58
## 266 267 268 269 270 271 272 273
## 46821.00 42927.49 51732.17 48005.02 52152.01 50268.47 54336.08 54632.12
## 274 275 276 277 278 279 280 281
## 60649.93 58284.02 67957.59 61881.99 62092.04 60441.01 66184.24 59914.33
## 282 283 284 285 286 287 288 289
## 56477.97 46029.62 44426.26 61659.59 67190.04 67599.52 65016.95 64104.26
## 290 291 292 293 294 295 296 297
## 68105.48 72015.97 53011.29 43112.70 37123.34 47445.40 50687.43 49801.33
## 298 299 300 301 302 303 304 305
## 73999.23 79945.03 80612.73 85225.49 83423.94 78453.87 81920.93 56818.07
## 306 307 308 309 310 311 312 313
## 53078.93 52758.68 46548.43 43757.68 47361.28 39912.97 38634.97 33196.13
## 314 315 316 317 318 319 320 321
## 36980.94 36162.23 39993.96 37978.25 63766.41 61607.43 63231.13 71229.83
## 322 323 324 325 326 327 328 329
## 73617.33 99100.85 97478.39 73306.82 72104.58 70461.78 62412.91 59534.93
## 330 331 332 333 334 335 336 337
## 29481.08 33168.92 33608.48 35928.59 35269.38 41036.83 42112.51 37359.72
## 338 339 340 341 342 343 344 345
## 36574.08 36700.76 32019.35 38018.81 38682.27 38940.74 39816.62 41602.69
## 346 347 348 349 350 351 352 353
## 43424.83 43176.47 36120.54 26687.59 32032.71 30893.62 30483.64 28099.88
## 354 355 356 357 358 359 360 361
## 32779.16 36533.77 40997.76 39186.61 40447.00 42585.65 49981.54 50532.18
## 362 363 364 365 366 367 368 369
## 50733.86 53197.25 50680.45 50124.94 42769.48 39966.01 36142.63 33921.12
## 370 371 372 373 374 375 376 377
## 29978.55 37267.12 39554.28 47440.53 41411.61 40858.73 39395.90 38928.64
## 378 379 380 381 382 383 384 385
## 29795.47 34394.00 27445.03 35665.13 45999.17 49564.95 47838.16 49830.03
## 386 387 388 389 390 391 392 393
## 56050.77 65537.13 58747.79 53215.47 52944.14 60324.44 60847.87 69517.39
## 394 395 396 397 398 399 400 401
## 58621.98 60189.03 59749.09 59232.90 57719.65 56481.09 43238.37 51824.90
## 402 403 404 405 406 407 408 409
## 50825.22 49791.34 56193.30 48735.00 48046.08 46372.58 42049.36 40879.95
## 410 411 412 413 414 415 416 417
## 38943.61 33037.13 40910.77 43837.43 38509.17 33596.88 48470.16 52315.38
## 418 419 420 421 422 423 424 425
## 56245.09 48713.41 45036.30 43712.13 47299.00 35716.80 35445.97 29704.11
## 426 427 428 429 430 431 432 433
## 35294.59 43623.14 50516.08 47281.15 44349.50 41274.33 41162.50 37639.91
## 434 435 436 437 438 439 440 441
## 33777.84 31011.56 32587.83 34437.43 32441.81 37308.55 43514.81 40266.79
## 442 443 444 445 446 447 448 449
## 39972.75 42979.53 40865.35 44855.34 40101.67 31132.87 29970.19 41328.49
## 450 451 452 453 454 455 456 457
## 41009.99 46661.88 42297.47 42647.18 44267.88 47986.55 37859.56 42709.45
## 458 459 460 461 462 463 464 465
## 38132.13 45702.13 49222.61 51843.50 48572.63 50913.70 51115.26 52862.52
## 466 467 468 469 470 471 472 473
## 52360.06 55304.19 52632.13 57683.96 50943.03 48553.66 47139.63 43763.55
## 474 475 476 477 478 479 480 481
## 47520.15 54984.44 49410.69 51114.14 45902.32 44281.11 47114.42 36527.28
## 482 483 484 485 486 487 488 489
## 30088.01 31957.57 34654.59 36144.75 37110.74 30750.72 43282.55 49943.60
## 490 491 492 493 494 495 496 497
## 56774.76 51485.56 56319.22 63953.87 67765.76 54020.33 44596.99 42621.72
## 498 499 500 501 502 503 504 505
## 42944.96 43736.22 38222.24 40621.48 45923.66 51605.99 52315.75 52426.33
## 506 507 508 509 510 511 512 513
## 46127.46 47467.61 43726.36 46482.16 46147.81 39862.79 40994.02 40169.06
## 514 515 516 517 518 519 520 521
## 41274.83 43915.19 36754.42 32033.20 55925.84 64087.53 67740.93 61119.19
## 522 523 524 525 526 527 528 529
## 62458.84 76046.15 83023.70 57970.88 52840.31 49534.24 53913.75 53420.02
## 530 531 532 533 534 535 536 537
## 43602.43 48594.84 61256.85 55777.07 59175.12 63163.36 60215.61 55245.78
## 538 539 540 541 542 543 544 545
## 48707.29 47363.03 55301.07 55051.46 47610.64 49834.52 49661.17 50355.08
## 546 547 548 549 550 551 552 553
## 41003.01 32837.82 37180.13 45297.64 45094.59 46800.66 40809.81 49823.79
## 554 555 556 557 558 559 560 561
## 50979.23 40757.27 50299.29 58162.96 57527.83 61125.93 56892.70 68654.49
## 562 563 564 565 566 567 568 569
## 85344.93 75434.85 63997.18 68416.87 66541.06 67727.82 59296.93 43284.30
## 570 571 572 573 574 575 576 577
## 50293.68 56199.41 57382.31 59458.05 60105.16 57231.29 69479.20 58851.50
## 578 579 580 581 582 583 584 585
## 52573.85 60176.17 61680.31 54773.65 61040.81 56616.51 53660.77 67232.22
## 586 587 588 589 590 591 592 593
## 52659.34 60004.32 59096.62 52818.22 52123.31 52399.37 43114.32 45911.43
## 594 595 596 597 598 599 600 601
## 40537.36 44783.57 53547.70 46878.29 52737.84 55115.86 60785.47 56943.24
## 602 603 604 605 606 607 608 609
## 61757.44 53325.30 55205.47 55981.88 58290.26 58864.35 58405.08 52581.96
## 610 611 612 613 614 615 616 617
## 59644.26 57704.18 54801.11 51506.90 44469.19 55980.50 59868.28 50802.88
## 618 619 620 621 622 623 624 625
## 61205.81 65391.11 58880.33 81109.83 66231.30 58559.96 60563.06 55915.98
## 626 627 628 629 630 631 632 633
## 46250.52 56958.84 37513.48 37373.70 46942.81 57426.36 55470.80 84202.22
## 634 635 636 637 638 639 640 641
## 74387.62 76588.54 78225.98 72951.88 65670.55 62304.58 50208.56 48576.75
## 642 643 644 645 646 647 648 649
## 47468.24 45950.37 44334.90 47011.95 51629.71 66601.71 81027.08 78150.22
## 650 651 652 653 654 655 656 657
## 79054.05 84925.96 98371.62 93129.84 63397.74 60849.37 57803.52 58788.22
## 658 659 660 661 662 663 664 665
## 55228.93 45640.98 48025.49 52343.59 51535.73 63098.34 62976.03 63265.57
## 666 667 668 669 670 671 672 673
## 51720.19 53079.81 54098.33 49436.77 43416.09 46451.21 44049.85 47536.88
## 674 675 676 677 678 679 680 681
## 45289.16 38126.27 32783.90 32781.41 35530.59 40262.92 42523.49 40527.88
## 682 683 684 685 686 687 688 689
## 40551.46 41000.26 35342.26 41672.08 41169.49 41468.77 43464.39 54172.84
## 690 691 692 693 694 695 696 697
## 62630.70 70680.44 59976.61 55984.12 52866.27 58020.68 48312.91 42011.55
## 698 699 700 701 702 703 704 705
## 35906.88 32625.77 31105.66 36612.64 34876.12 35548.81 41482.13 70143.41
## 706 707 708 709 710 711 712 713
## 76302.37 93850.58 90169.61 92765.04 107791.09 106631.65 83992.18 84339.51
## 714 715 716 717 718 719 720 721
## 75677.35 72779.90 70756.32 53485.05 48883.14 52375.41 49880.08 38994.66
## 722 723 724 725 726 727 728 729
## 44562.92 40834.02 43079.25 40954.33 31661.16 35612.09 36237.36 29872.59
## 730 731 732 733 734 735 736 737
## 47941.12 50188.84 48222.68 53876.81 46283.10 46557.66 54539.39 40990.40
## 738 739 740 741 742 743 744 745
## 37402.28 45903.82 42678.25 44180.89 46949.67 46062.82 42481.81 49470.84
## 746 747 748 749 750 751 752 753
## 44491.78 65454.64 70775.04 66884.27 58819.43 78598.89 71673.01 70592.20
## 754 755 756 757 758 759 760 761
## 55829.12 104550.17 121621.01 126210.06 107735.67 110163.99 109411.42 107440.64
## 762 763 764 765 766 767 768 769
## 60117.76 45168.59 47077.85 45702.38 43678.93 152251.70 156688.61 181893.19
## 770 771 772 773 774 775 776 777
## 185459.72 179397.36 177394.75 184276.95 81493.97 72083.11 38457.62 41890.61
## 778 779 780 781 782 783 784 785
## 42299.60 46697.32 41096.11 41416.11 41258.73 45812.33 40549.59 40247.07
## 786 787 788 789 790 791 792 793
## 45361.04 48385.80 46640.29 43989.57 46728.27 50095.86 50501.73 45045.41
## 794 795 796 797 798 799 800 801
## 41081.38 41666.34 42076.45 36708.99 36790.74 36063.38 35954.05 47556.22
## 802 803 804 805 806 807 808 809
## 50285.44 56897.94 59046.44 53610.72 60772.86 68506.73 57377.19 50451.31
## 810 811 812 813 814 815 816 817
## 44303.58 48113.60 52481.74 50652.49 38661.80 36966.34 44544.20 52894.60
## 818 819 820 821 822 823 824 825
## 44545.70 38929.51 32635.63 43812.85 43991.32 41396.39 35568.41 44734.15
## 826 827 828 829 830 831 832 833
## 52778.78 57242.03 54085.48 53415.28 55978.01 59769.56 60423.41 53727.54
## 834 835 836 837 838 839 840 841
## 55548.81 54967.47 57212.70 60386.59 58549.85 49782.35 55235.05 50794.14
## 842 843 844 845 846 847 848 849
## 44540.45 48336.25 37617.94 37184.50 49427.41 51112.64 51994.76 51632.83
## 850
## 55836.86
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8093
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.262042 0.7879612 4.009273
## t2* 2715.568547 164.8153807 887.557418
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.187809 5.253548 13.73319
## 2 lag_depvar 1667.507919 2753.649754 4539.71669Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 11 01:02:47 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 11 01:02:57 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 11 01:03:07 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 11 01:03:16 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 11 01:03:26 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 11 01:03:36 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 11 01:03:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 11 01:03:55 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 11 01:04:05 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 11 01:04:14 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 8.205857 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 192.101143 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 54.522571 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 1.884286 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 26.407143 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 14.927000 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 15.711429 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 313.759429 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2777, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2777 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-08-09 00:04:58 sería de: 26.443 pesos// Percentil 95% más alto proyectado: 34.867,93
Según TimeGPT: La proyección de la UF a 298 días más 2026-06-03 sería de: 39.627,14 pesos// Percentil 80% más alto proyectado: 39.944,44 pesos// Percentil 95% más alto proyectado: 40.252,42
Según prophet: La proyección de la UF a 298 días más 2026-06-03 sería de: 42.418 pesos// Percentil 95% más alto proyectado: 51.833
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26067.78 | 26331.96 |
| Lo.80 | 26196.95 | 26493.62 |
| Point.Forecast | 26442.72 | 26799.01 |
| Hi.80 | 31244.83 | 32070.87 |
| Hi.95 | 34130.49 | 34861.62 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4788 1028.6175
## s.e. 0.1010 41.4439
##
## sigma^2 = 38077: log likelihood = -521.14
## AIC=1048.28 AICc=1048.61 BIC=1055.35paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4669 735.1797 9.0494
## s.e. 0.1009 315.9266 9.6535
##
## sigma^2 = 38168: log likelihood = -520.71
## AIC=1049.42 AICc=1049.97 BIC=1058.85forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 611.0632 | 592.9478 | 566.8658 |
| Lo.80 | 760.9455 | 743.7341 | 659.1232 |
| Point.Forecast | 1044.0800 | 1028.5762 | 876.3411 |
| Hi.80 | 1327.2145 | 1313.4183 | 1165.1444 |
| Hi.95 | 1477.0969 | 1464.2046 | 1354.7715 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.3 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.11.0
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.1.0
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.9.0 lattice_0.22-6
## [34] GGally_2.3.0 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.1 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.4 data.table_1.17.8
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 httr2_1.2.1
## [4] lifecycle_1.0.4 StanHeaders_2.32.10 doParallel_1.0.17
## [7] globals_0.18.0 vroom_1.6.5 MASS_7.3-60.2
## [10] crosstalk_1.2.1 magrittr_2.0.3 sass_0.4.10
## [13] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
## [16] fracdiff_1.5-3 doRNG_1.8.6.2 askpass_1.2.1
## [19] pkgbuild_1.4.8 DBI_1.2.3 abind_1.4-8
## [22] quadprog_1.5-8 nnet_7.3-19 rappdirs_0.3.3
## [25] sandwich_3.1-1 inline_0.3.21 data.tree_1.1.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.12
## [31] spatial_7.3-17 parallelly_1.45.1 codetools_0.2-20
## [34] xml2_1.3.8 tidyselect_1.2.1 farver_2.1.2
## [37] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [40] stats4_4.4.0 jsonlite_2.0.0 ellipsis_0.3.2
## [43] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.3
## [46] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [49] xfun_0.52 TTR_0.24.4 ggfortify_0.4.19
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.3
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [64] networkD3_0.4.1 gtools_3.9.5 generics_0.1.4
## [67] htmlwidgets_1.6.4 ggstats_0.10.0 pkgconfig_2.0.3
## [70] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [73] S7_0.2.0 selectr_0.4-2 janeaustenr_1.0.0
## [76] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [79] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [82] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [85] curl_6.4.0 cachem_1.1.0 KernSmooth_2.23-22
## [88] parallel_4.4.0 fBasics_4041.97 pillar_1.11.0
## [91] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [94] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [97] evaluate_1.0.4 mvtnorm_1.3-3 cli_3.6.5
## [100] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [103] future.apply_1.20.0 labeling_0.4.3 rstan_2.32.7
## [106] QuickJSR_1.8.0 viridisLite_0.4.2 assertthat_0.2.1
## [109] lazyeval_0.2.2 Matrix_1.7-0 hms_1.1.3
## [112] bit64_4.6.0-1 future_1.67.0 nixtlar_0.6.2
## [115] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [118] bslib_0.9.0 quantmod_0.4.28 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))